Power Shift System to Store and Distribute Energy

ABSTRACT

Disclosed is a machine learning energy management system that regulates incoming energy sources into compressed air storage operations and energy generation. Compressed air is directed into a thermoregulation system that cycles storage tanks according to physical qualities. A boost impulse creates energy to initiate the electrical energy generation. The compressed air operations and energy generation leverage the heating and cooling of an external HVAC system to improve performance and conservation of the heating and cooling for an external building. The system combines real-time data, historical performance data, algorithm control, variable air pressure for demand-based generation, tank-to-tank thermal cycling, building air heat exchanger, and boost pulsation to achieve optimized system efficiency and responsiveness.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of and is a continuation of U.S.patent application entitled “Power Shift System to Store and DistributeEnergy,” having Ser. No. 17/566,682, filed Dec. 31, 2021, and currentlypending, which is incorporated by reference in its entirety as if fullyset forth herein.

FIELD OF THE INVENTION

The present invention generally relates to reduction of greenhouse gasemissions, related to energy generation, transmission, and distribution.More specifically, the present invention is a machine-learningmanagement system that controls the collection of renewable resourcesand low-peak energy and determines when to transmit energy for directusage or store as compressed air. The system also determines thegeneration of energy from stored compressed air and controls thedistribution of energy according to forecasted data and predicteddemand. The present invention enables the reduction of greenhouse gasemission by increasing the application of renewable energy and reductionof renewable resource curtailment.

BACKGROUND OF THE INVENTION

There is a critical need for safe and reliable power and an increasedglobal commitment to a carbon-free future. With the majority of theworld's electricity generated from fossil fuels, a global trend hasemerged towards a more thoughtful usage of power generation. Primaryhistoric sources of electricity—oil, gas, coal, and nuclear—are derivedfrom non-renewable technologies. Renewable technologies, such as solarand wind, generate electricity on an intermittent and unpredictablebasis. While demand for electricity is predictable on a daily basis,supply of electricity produced from renewable energy does not matchdaily demand, resulting in heavy reliance on non-renewable technologiesthat are not environmentally sustainable and often include oldergeneration natural gas-fired plants that produce high rates ofgreenhouse gases.

FIG. 1 shows the collection of solar and wind energy over a 24-hourperiod on Mar. 6, 2018, reported by the California Independent SystemOperators. The wind energy reported is almost negligible throughout thefull period, and the solar energy is reported to be most availablebetween the hours of 8 am and 4 pm with negligible availability outsidethis timeframe. Due to the growth of solar energy production, the peakenergy demand has shifted to later in the day, causing further issueswith solar over-generation in California.

The world's demand for power does not consider the availability of thesource and, hence, the availability of energy from renewable resourcesdoes not naturally match the demand schedule for power. FIG. 2illustrates the demand curve over a 24-hour period and also illustratesthe net demand curve of the resulting energy demand after applying theenergy provided by solar and wind. The net demand curve essentiallyreflects the amount of non-renewable power required to meet the netenergy demand.

During the periods of surplus energy, the overproduction of energy fromrenewable resources must be backed off in curtailment to ensure that thesupply and demand remain in balance. This curtailment results in amassive waste of resources from both the overproduction and from thecurtailment of unusable renewable energy. Unforeseen imbalances betweenscheduled supply and actual production are heavily penalized due to thesupply deviation from its committed schedule in the power markets. In2017, California alone reports having to curtail 379,510 MWH ofoverproduction in order to maintain system supply and demand balance orsystem frequency. FIG. 3 shows the California ISO's historicalcurtailment of renewable resources from 2014 through 2017.

This problem of imbalance can be mitigated by storing energy when it isat a surplus and applying it to a later time period of peak demand whenthere are insufficient renewable sources. This would smooth out theincongruity of the supply and demand curves to reach a more balancedsystem, tailoring the supply to a scheduled demand as shown in FIG. 4 .Not only would this prevent curtailments of wasting and disposing ofpower, it would also minimize commercial contracting for the heightenedprices of energy during those peak periods.

Existing short-term storage technologies, while viable, suffer from avariety of shortcomings that limit their widespread use across a utilitygrid, especially for residential, small commercial, and micro-gridapplications. Existing storage technologies include rechargeablebatteries (lithium and lead-acid), flow batteries, inertial (flywheel),pumped water, gravitational potential energy, and traditional compressedair energy storage systems, all having shortcomings of high initialmaterial acquisition and lifecycle costs, high infrastructure costs, lowenergy density, low or no portability, and potentially high end-of-lifedisposal costs, especially for battery-based storage. Lithium-basedenergy storage cannot easily respond to the afternoon energy demandramp, and therefore is often still dependent on natural gas energyplants. Older, dirtier plants must be kept online to meet this demandand pump storage is utility scale, not small scale, and thereforerequires complex site planning and costly development.

Compressed air energy storage (CAES) is a proven technology withcurrently operating plants of various sizes but has widely recognizedshortcomings based on the mechanical losses of the system andfundamentals of thermal inefficiency during the compression andexpansion processes. In traditional systems, excess or off-peak power isused to spin a chain of air compressors that force high-pressure airinto tanks. Compression creates heat that is either stored adiabaticallyin an enormous thermal mass or dissipated diabatically into theenvironment. When energy demand is high, compressed air is released fromthe tanks at a regulated pressure. The released air spins an air motorexpander and generator to feed electricity back into either a utilitygrid or a micro-grid. Expansion removes heat from the compressed air,which will approach inefficient or potentially damaging cryogenictemperatures. Consequently, the air must be warmed during the expansionprocess. An adiabatic process warms the air with heat stored during thecompression process. A diabatic process reheats the air with a gas firedburner or other heat source, consuming non-renewable energy that createsgreenhouse gases. Large-scale CAES plants of at least 100 MWh storagecapacity use underground caverns for compressed air storage, relying ongas-fired burner or heat from gas-fired plants. Additionally, since aCAES system uses mechanical components by design, mechanical losses dueto friction, inertia, and component design are unavoidable.

A CAES system that could overcome thermal and mechanical inefficiencieswould have broad application for both residential and commercialmicro-grids.

SUMMARY OF THE INVENTION

Disclosed is a machine-learning energy management system that controlsenergy capture, air compression, storage, and electrical energygeneration and distribution to provide safe, reliable, efficient energyfrom intermittent and off-peak sources for individual demand. Thepresent invention harnesses energy from renewable sources and from apublic utility grid during low demand and low price periods and convertsthe energy to compressed air, which is stored for later usage. Thecompressed air is converted to electrical energy and distributed using acontinuum of optimal operating parameters that enable peak demands ofdaily commercial and residential usage to be met by shifting energy totimes of required demand and to times when direct renewable energysources are not available. The present invention creates grid resiliencyand independence by harnessing, storing, and distributing power on amicrosystem level according to specific individual demand on amicro-grid kilowatt residential and commercial scale, also reducingcurtailment of excess renewable energy.

The disclosed invention comprises a system to manage capture ofrenewable energy and grid energy, determining when excess renewableenergy is available and considering current and anticipated supply anddemand, current and anticipated energy costs, current and forecastedweather, curtailment data, and historical system performance. The systemconverts excess renewable energy to stored energy in the form ofcompressed air, using a continuous cycling of storage tanks and a boostimpulse to minimize efficiency losses due to heat of compression. Thesystem also captures waste heat from the compression process andtransfers the heat to an HVAC (heating, ventilating, air conditioning)system for use in heating an external budding. The system determineswhen to convert compressed air to electrical energy considering overallsupply and demand factors, further controlling the release of compressedair to power an air motor that is mechanically coupled to a generator,using controlled thermal environmental heating to offset operationalcooling. The system captures remaining expansive cooling energy andtransfers it to the HVAC system to also cool the building. The systemconverts mechanical rotation to electrical power and controls anelectrical system to feed that electrical power onto an electrical gridas a source of generated electricity. When capturing energy from a windsource, the system shifts the input to a mechanically-coupledtransmission to convey rotational energy from the wind vanes directly tothe compression system to improve overall system efficiency.

The present invention advances the technology of existing, large-scale,reliable CAES technologies by enabling a scalable system that leveragesproven reliability of a traditional system with an innovative isothermalprocess that harnesses heat from the surrounding environment to warm theair, significantly increasing system efficiency and reliability whileminimizing complexity. The present invention does not rely on fossilfuels, instead using engineering innovations to minimize thermalinefficiencies, increase overall efficiency, and minimize totallifecycle costs with none of the environmental justice concerns oflithium technologies, either in raw materials acquisition or inend-of-life disposal. The present invention provides a customer side ofthe meter solution with increased cycle performance and critical energyneeds, including resiliency, reliability, improved safety, lowerenvironmental impact, lower costs than currently fielded systems, andbetter long term and lifecycle performance.

The present invention has widespread residential, commercial, andindustrial applications, while creating a grid independence opportunitythat minimizes the impact of utility shutdowns. The present inventionalso brings power to isolated areas that are currently unreachable byutility services and enables an integrated system for larger industrialand agricultural megawatt scale applications.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing summary, as well as the following detailed description ofpreferred embodiments of the invention, will become better understoodwhen read in conjunction with the appended drawings. For the purpose ofillustrating the invention, there are shown in the drawings embodimentsthat are presently preferred. It should be understood, however, that theinvention is not limited to the precise arrangements andinstrumentalities shown. In the drawings:

FIG. 1 illustrates the typical collection of solar and wind energy overa 24-hour period on Mar. 6, 2018, reported by the California ISO;

FIG. 2 illustrates the typical demand curve over a 24-hour period andthe net demand curve of resulting energy demand after applying theenergy provided by solar and wind;

FIG. 3 illustrates the California ISO's typical historical curtailmentof renewable resources from 2014 through 2017;

FIG. 4 illustrates Application of Stored Energy to Demand Curve;

FIG. 5 illustrates the System Diagram;

FIG. 6 illustrates the Operation Flow, Part 1;

FIG. 7 illustrates the Operational How, Part 2;

FIG. 8 illustrates the Operational Flow, Part 3;

FIG. 9 illustrates the Operational Flow, Part 4;

FIG. 10 illustrates the Energy Storage;

FIG. 11 illustrates the Energy Generation; and

FIG. 12 illustrates the Controller System.

DETAILED DESCRIPTION OF THE INVENTION

As illustrated in FIG. 5 , the present invention uses a controllersystem 500 to manage incoming energy sources 501 into compressed airoperations 502 and energy generation 504. Compressed air is regulated tofeed into a cycling system of storage tanks 503 according to temperatureand also generates boost pulsation 505 to create energy forkick-starting the mechanical components of the energy generation. Boththe compressed air operations and energy generation have controlledoutlets to an HVAC system 506 of an external building to leverage theheating and cooling of the system to improve performance andconservation of the heating and cooling operations for the building.Energy generation is exported to an electric utility grid 507. Thesystem combines near real time web-based data, historical performancedata, algorithm control method, variable air pressure for demand-basedgeneration, thermal tank cycling, building air heat exchanger, and boostpulsation to continually calculate and analyze optimal operatingparameters that enable optimized system efficiency and responsiveness.These engineering innovations represent a significant advancement andevolution over the systems currently interconnected with grid powersystems.

System Operation Flow

The present invention begins with the flow illustrated in FIG. 6 . Thesystem operation runs in a continuous loop consisting of a series ofdecisions and processes. At the start 1, the system determines 2 whetherto store energy, generate energy, or do nothing, and commands thecontroller appropriately when there is excess energy available fromdifference energy sources that include utility grid, a solar cell, or awind turbine using results from CPU Analysis 3. When excess energy isavailable or when energy is determined to be at a desirable low cost,the system executes its storage process via the controller using theavailable excess energy to spin an electric motor connected to an aircompressor. The system uses web data 5 to calculate upcoming energydemand, determine optimal storage 4, and determine 7 when energy shouldbe stored 6. Once it is determined that energy is to be stored, the CPUruns 8 a Proportional-Integral-Derivative (PID) algorithm usinghistorical performance data 9, CPU analysis 10, and internet data 11 todetermine the energy source 13 and storage operation parameters 12.

When capturing energy from a wind source, the system shifts 15 the inputto a mechanically-coupled transmission to convey rotational energy fromthe wind vanes directly to the compression operations, bypassing thewind electrical generator and the compressor electrical motor andtherefore bypassing the losses that would exist between thosecomponents. There is a mechanical clutch between the wind turbine andthe compressor that is engaged to directly spin the compressor, the tankoutlet valves are closed, and the tank fill valves are opened. For theutility grid or solar source selection 14, the power relays are closed,the tank outlet valves are closed, and the appropriate tank fill valveis opened.

The compressor draws outside ambient air into its compression chambers,compresses it to a higher pressure, and transfers 16 that high-pressureair into a cycling array of storage tanks for later use. Each storagetank in the cycling tank array is filled one at a time using athermoregulation process 42 to mitigate the heat generated fromcompression operations, while the system continuously monitors 18 thetemperature, pressure, and electric current of each storage tank usingsensors coupled to each tank. The heat generated during compressionoperations warms the compressed air and also the tank being filled. Ifnot removed, this heat expands the air and increases its volume,partially countering the work done to compress it.

While the heat of compression is partly expelled at each compressionstage, the compressed air still carries increased heat energy. Tomitigate this thermal effect, the controller monitors 18 thetemperatures of each storage tank in the cycling tank array. The systemrelies on a cycling algorithm to operate a thermoregulation process 42that continuously rotates through the tank array to increase efficiencyof the system. When the temperature of the storage tank currently beingfilled with air reaches a specified temperature threshold, thecontroller closes 42 that tank's fill valve and shifts operations tofill a cooler tank in the array 17. This allows the warmer tank topassively cool itself using ambient air as an environmental force thatchanges the temperature of the tank, which helps to maximize systemefficiency by storing cooler air and leveraging the ambient air to coolthe tanks on a continuous rotating basis. The system also pressurizes aseparate boost tank 31 with high-pressure air that is used to provide aboost impulse at the beginning 32 of the energy generation process.Alternatively, the boost impulse 31 may also comprise a springconfiguration that uses recoil power as a catalyst for the initiation ofthe air motor.

The system monitors 18 the storage tanks in the cycling tank array 17 todetermine when they are full of compressed air, updating measurements ofpressure, temperature, and electrical current. The results of the tankmonitoring 18 are integrated 19 in the historical performance data 9 tobe used in the PID algorithm 8 to refine overall operating parameters.

As shown in FIG. 7 , to minimize this impact, the compressor radiates21, 22 this heat into a coil of tubing filled with a circulating fluid.Using a multi-stage compression process, heat is transferred into thefluid at each compression stage. The system is connected to an HVACsystem of an external building. When the system determines 20 that theHVAC system is signaling for building heat, the fluid is circulatedthrough a heat exchanger that is coupled to the existing HVAC system ofthe building. The system process configures 21 valves to circulate fluidthrough a coil to capture waste heat from compressor operations, thencirculates that liquid through the HVAC heat exchanger to transfer 43 tothe building air for use as supplemental heat when needed. When thesystem determines 20 that supplemental heat is not needed, the system 22configures valves to circulate fluid through a coil to capture wasteheat from compressor operations, diverting the waste heat by circulatingthe fluid through a radiator to transfer 44 the unneeded heat to ambientair. The use 17 of an array of smaller tanks, rather than one largetank, increases the tank surface area to tank volume ratio, increasingheat transfer to ambient air and facilitating the tank cooling process.This heat transfer therefore increases the potential amount ofhigh-pressure air that can be stored in the air storage tanks.

Once the system determines 23 that the tanks are full, the systemprocess turns off 45 the compressor by opening the power relays betweengrid and/or solar panels or by disengaging the mechanical clutch at thewind turbine. The process then closes the tank fill valves and returnsto the start 1.

As shown in FIG. 8 , when the system determines 2 there is no excessenergy available from a utility grid, a solar cell, or a wind turbineusing results from CPU Analysis 3, the system operation queries 24 webdata 5 to calculate upcoming demand and determine optimal energygeneration. The system determines 7 whether energy should be generated26. If no energy should be generated, the process delays 25 for aspecific duration, then returns to the start 1. However, when the systemdetermines 7 a demand for energy, it shifts to a generation process. Theprocess uses historical performance data 9, CPU Analysis 10, andinternet data 11 to continuously run 27 algorithms for machine learning,artificial intelligence, neural networks, orProportional-Integral-Derivative (PID) control to determine optimaloperating parameters 28, including optimal air pressure to maintaincorrect air motor rotational speed.

When the energy generation process is initiated, the at-rest air motorand connected generator 33 represent an inertial mass that must beginspinning. To overcome this at-rest inertia, the system uses the boostimpulse 31, which includes releasing 29 an impulse of high-pressure airfrom the boost tank 31 or using a spring configuration to kick-start theair motor. When a boost tank is used, the system opens the boost tank 31to kick-start rotation of an air motor 32. When a spring configurationis used, the system engages the spring configuration to act as thecatalyst to initiate the air motor. This impulse immediately beginsrotating the air motor and generator from the resting position to anoperational speed. Once the air motor reached the operational speed, thehigh-pressured air from the cycling tank array is used to maintain thisrotation from the operational speed. The system sets 29 the air tankoutlet pressure regulator 30 to optimal pressure for energy generationand controls 29 the openings of the tank outlet valves to maintainrotation of the air motor 32. When the high-pressured air is releasedthrough the valves, the air motor converts 32 the high-pressured airinto rotational motion using a mechanical arrangement of pistons,valves, and vanes.

As shown in FIG. 8 , the air motor rotates a mechanical shaft which isconnected to the generator. This rotation spins the shaft and armaturesof the generator, creating electricity and effectively converting thehigh-pressure air back into energy. With the generation process running33, electrical energy is delivered 34 to the desired source (microgrid,building, or utility grid) via a grid tie inverter and associated powerrelays. The system continuously measures 35 pressure, temperature, andelectrical current. The results of those measurements are sent back 36to be used as inputs 9 to the PID algorithm 27 to refine overalloperating parameters.

As the generation process continues, the controller calculates 27 theoptimum rotational speed of the air motor to meet the energy demand,based upon a variety of parameters including current energy demand 24,short-term demand forecasting 10, and historical system performance 9.These parameters are gathered from web-based information sources andfrom historical metrics stored in system memory.

As the high-pressure air is released through the air motor 32, itdecompresses and gives up its thermal energy. This cooling effect willdrop the temperature of the air motor and associated valvessignificantly, eventually impacting performance. Cool air has lessvolume, impacting efficiency. If the air temperature drops far enough,the system will begin to freeze, impacting mechanical performance. Thesystem uses a thermoregulation process 48 to mitigate this thermaleffect. The controller monitors the temperatures of each storage tank inthe cycling tank array 17 and, when the temperature of the active tankcurrently being filled with air reaches a specified temperaturethreshold, the controller closes that tank and shifts operations to filla cooler tank in the array 17, continuously rotating from cold to warmtanks. This allows the colder tank to passively warm itself usingambient air as the environmental force to maximize system efficiency.The use of an array of smaller tanks increases the overall tank surfacearea to tank volume ratio, increasing heat transfer from ambient air andfacilitating the tank re-warming process.

As shown in FIG. 9 , as long as the generation demand has not beenfulfilled, the system updates 35 measurements of pressure, temperature,and electrical current. This data is sent back 36 to be incorporated asinputs to the control algorithm to refine overall operating parameterswhile the air motor rotation process 32 and generator process 33continue. The controller commands a variable pressure air regulatorvalve to set the proper air pressure arriving at the air motor in orderto manage 32 the optimum air motor rotational speed and therefore theoptimum output 33 from the generator without wasting any system energy.In addition, a coil of tubing filled with a circulating fluid isthermally coupled to the air motor 32, capturing the cooling resultingfrom the air expansion. The fluid is circulated through the heatexchanger 38 that is coupled to the existing HVAC system of an externalstructure. While the generator is operating 33, the system determines 37whether the building HVAC system is calling for temperature cooling.When the building needs cooling, the system configures 38 valves tocirculate fluid through the coil to capture cooling from air motor airexpansion, then circulates 46 that liquid through the HVAC heatexchanger to transfer this cooling energy to the building for airconditioning when needed. When the system determines 37 no cooling isneeded, the system diverts 39 the waste cooling to an external radiatorby configuring valves to circulate fluid through the coil to capturecooling from air motor expander operation, then circulates 47 thatliquid through a radiator to warm the liquid from ambient air.

The system continues the generation process until it determines 40 thatthe generation demand has been fulfilled or until all the storedcompressed air has been used. At this point, the system turns off 41 theair motor by opening the power relays and closing the tank valves andwaits to begin the storage process or generation process.

System Integration

As illustrated in FIG. 10 , the controller system regulates thecollection of energy from energy units 110 wired to an inverter 120 thatdelivers energy to an AC breaker panel 130 and an electrical utilitygrid 135. The utility grid 135 is the existing interconnected networkfor delivering electricity from producers to consumers, comprisinggenerating stations that produce electric power, electrical substationsfor stepping electrical voltage up for transmission or down fordistribution, high voltage transmission lines that carry power fromdistant sources to demand-centers, and distribution lines that connectindividual customers to the grid. The energy from the panel 130 chargesa variable speed multi-stage compressor 140 that compresses air to thedesired high-pressure range. Alternatively, a windmill 300 ismechanically coupled to the compressor 140 through a clutch 310. Theclutch allows the spinning windmill 300 to directly drive the compressor140 to minimize electrical losses that would otherwise be present.

An output air hose 141 feeds into an actuator valve 142 with a pressuresensor 143, then feeds through an inlet track section of amulti-connector 144. One of the outlet track sections is connected to atleast one secondary actuator valve 145, each valve with a connectedpressure sensor 146, and feeds via air hoses 51 into storage tanks 50equipped with temperature sensors 52. The output air hose 141 may alsofeed a boost tank 96 through the multi-connector 144 and an actuatorvalve 97 with a pressure sensor 98.

The boost impulse 96 comprises an apparatus that acts as a catalyst forkick-starting the air motor from a resting position to an operatingspeed. Once the air motor is running at the desired operational speed,the boost impulse ceases and the operation of the air motor issubsequently taken over by the cycling tank array. When an auxiliaryboost tank is used as the boost impulse, the boost tank provides a shortrelease of pressurized air for the start-up of energy generation. Thispulsation kickstarts an expander and mechanically coupled generator,using the pulse to overcome mechanical at-rest inertia without depletingair in the cycling tank array 50. The boost tank 96 is quickly rechargedat the beginning of each storage cycle, as needed, by air pressure fromthe compressor 140 through the multi-connector 149. The boost impulsemay also be comprised of a spring apparatus that uses a recoil power asthe catalyst for the air motor to reach the operational speed.

A heat exchanger coil 200 gathers waste heat from the compressor 140using a circulating liquid, which is sent to a valve 201 that directsthe heated liquid to an HVAC heat exchanger 240. Circulating air 250from the building's HVAC system captures this heat from the heatexchanger 240, This otherwise wasted heat is used to heat the building.When building heat is not needed, the valve 201 directs the heatedliquid to a radiator 220 that radiates the excess heat to ambient air.

As illustrated in FIG. 11 , energy generation is also managed by thecontroller system to regulate the production of energy. The compressedair in the storage tanks 50 is released through second actuator valves520 via air hoses 530 with the high-pressure output feeding through themulti-connector 149. The output hose at the multi-connector 149 directsthe high-pressured air at A into a variable pressure regulator 147 witha sensor 93, transmitting the air at a variable low pressure B into anexpander 60. The shaft of the expander is connected via a flexiblecoupling 61 to an electric generator 70. At the beginning of ageneration cycle, the controller initiates the boost impulse to kickstart the expander 60 and generator 70. This kick-start overcomes theat-rest mechanical inertia of the expander and generator. Once theexpander and generator are spinning, the rotation is maintained withoptimized air pressure from the cycling tank array 50. The generator iswired to return power to the AC breaker panel 130 and electrical utilitygrid 135. When a boost tank of high-pressured air is used, thecontroller momentarily opens an actuator valve 540 of the boost tank 96equipped with a pressure sensor 98 to provide a puke of pressurized airin order to start up the expander. Alternatively, the controller mayactuate a spring configuration to provide startup power for theexpander.

A heat exchanger coil 210 gathers waste cooling from the air motor 60using a circulating liquid that is sent to a valve 202. When the buddingassociated with the system k cooling its interior, the valve 202 directsthe cooled liquid to the HVAC heat exchanger 240. Circulating air 250from the building's existing HVAC system captures this cooling from theheat exchanger 240, and this otherwise wasted cooling is used to helpcool the building. When building cooling is not needed, the valve 202directs the heated liquid to a radiator 230, which radiates the excesscooling to ambient air.

As shown in FIG. 12 , the system operation is managed by a controllersystem 500 comprising a central processing unit (CPU) 80, programmablelogic controllers (PLC) 400, an automated control program 90, and aWireless Internet Gateway 410 that interfaces with multiple web datasources 420 using the internee 430 and specific cloud servers 440. Thecontroller continuously monitors parameters related to the existing andanticipated availability of energy and the existing and anticipateddemand for energy for the current 24 to 48 hours. These parametersinclude web-based information such as weather forecasts, utility gridenergy pricing, curtailment data, and utility forecasting. Energysources include excess utility grid energy and energy from renewablesources, including solar and wind. Supply and demand forecasting isimplemented using internet-based web data sources queried by thecontroller 500. These sources include current supply and demand data,forecasted supply and demand, supply and demand trends, current marketpricing and pricing trends, curtailment data, and current and forecastedweather. The controller 500 combines this data with historical systemperformance data and determines a suitable system performance usingtailored algorithms for machine learning, artificial intelligence,neural networks, and PID control to continually optimize the overallsystem operation for maximum efficiency and power generation. Thealgorithms are specifically directed toward minimizing the energy-losseffects of diabatic cooling during power generation by automaticallyswitching from the air tanks cooled by air decompression to warmer airtanks based on a temperature comparison. This cycling allows the colderair tanks to warm via ambient air energy before being called upon todeliver compressed air to an air motor, thereby increasing theefficiency of energy generation.

The controller system 500 is wired to the breaker panel 130, thecompressor 140, the actuator valves 142, 145, 97, 520, 540, 201, 202,the pressure sensors 143, 146, 92, 93, 98, the temperature sensors 52,the clutch 310, and the variable pressure regulator 147. The controller500 is directed by a user-input demand schedule or a pre-programmeddefault automated control program 90 generated either on-site or at aremote location. It controls the energy storage and the output ofproduction to reshape the distribution of energy supply to thedistribution of the demand/supply schedule in order to maintain abalanced system. The controller 500 is also wired to the panel 130 toreceive data used to evaluate what is needed based on the forecastedschedule 90 to determine when to store collected energy, when to releaseit, and the rates needed to meet the desired power production.

The controller 500 uses the results of the system algorithms todetermine optimal performance. The controller decides whether to donothing or to store or generate energy, determines how quickly to storeor generate energy, and manages air tank temperatures. For energygeneration, the controller uses the algorithm results to select a properair pressure using the variable air pressure regulator 147, whichenables the system to modulate the power generation rate in order toclosely match the actual demand load without wasting stored energy(compressed aft). During power generation, the controller 500 alsoshifts air valves to draw compressed air from specific tanks within thecycling tank array 50 on a rotating basis in order to keep air drivingthe motor 60 at a more constant temperature and mitigate any undesiredeffects of expansion cooling in a typical diabatic system. In thismanner, the system is able to maintain a more constant operating airtemperature by constant heat exchange from the environment,approximating constant temperature of an isothermal system.

The controller 500 is a robust, fault tolerant combination of a CPU, webaccess gateway via cellular technology, and a PLC that embodies astandard, well-proven industrial digital computer specifically designedfor this control system. The CPU contains a monitoring and analysisprogram that gathers data from web sources and compares the data withhistorical performance data to decide when to store energy and when togenerate electricity. The CPU updates the data and its resultingdecision with specified frequency. The CPU monitors operating parametersof the system in real time, including air pressure at various points,temperature at various points, voltage and current at various points,and ambient air temperature.

When a decision is made to store or generate energy, the CPU runs acontrol algorithm to optimize the overall system performance. Thealgorithm is a control-loop mechanism employing feedback via a varietyof pressure, temperature, and electrical sensors to optimize overallsystem performance and keep the performance within the desired parameterlimits, The control algorithm continuously calculates an error value forpressure, temperature, and electrical parameters, as the differencebetween measured values and desired setpoints. The algorithm thenapplies an appropriate correction by varying voltage, current, or airpressure based upon proportional, integral, and derivative terms thatare calculated from measured values and historical performance data. Theproportional value represents the current measured error value for aparticular parameter, the integral value accounts for past values of theerror for a particular parameter by integrating over time, and thederivative value is an estimate of the future trend of the error valuefor a particular parameter calculating the derivative rate-of-change ofthat value.

The three algorithm coefficients are continuously combinedmathematically to calculate optimal operating parameters for voltage,current, and air pressure in order to maintain optimal overall systemperformance and to adjust respective controlled devices if theparameters deviate from their desired setpoints. When a correction isnecessary, the control algorithm selects the minimal change to drivedesired behavior without overshooting a parameter. This feedback controlsystem helps to optimize overall system performance, and thereforesystem efficiency, and minimizes wear and tear on system components tomaximize system reliability,

The control algorithm is used during the energy storage phase to decidehow quickly to compress air and fill the cycling tank array. This rateof storage varies based upon available renewable energy, the time windowthat the energy will be available, and the optimal operating parametersof the compressor. The control algorithm is used during the energygeneration phase to direct the proper start-up and ramp of the air motorvia boost impulse and air pressure regulation to conserve air pressureas much as possible. The algorithm then varies the air pressure to keepthe air motor within the optimal RPM (revolutions per minute) band,thereby maintaining constant generator speed. This approach optimizesoverall power generation while conserving as much air pressure aspossible.

Grid-tie inverters 600 shown in FIG. 11 are normally 120 V RMS(root-mean-square, a standard measure of effective AC voltage) at 60 Hzor 240 V RMS at 60 Hz, and used between electrical power generators,including solar panel, wind turbine, hydro-electric, and the utilitypower grid 135. To inject electrical power efficiently and safely intothe grid 135, the grid-tie inverter 600 accurately matches the voltageand phase of the grid sine wave AC waveform. The electrical power grid135 is the existing interconnected network for delivering electricityfrom producers to consumers, comprising generating stations that produceelectric power, electrical substations for stepping electrical voltageup for transmission or down for distribution, high voltage transmissionlines that carry power from distant sources to demand-centers, anddistribution lines that connect individual customers to the grid. Duringenergy generation, the system essentially becomes a supplementalgenerating station for the overall grid 135, replacing the solar or windor higher priced energy that is no longer available or decreased due tolack of sun, wind, or desirability.

1. An energy management machine comprising: at least one controlcomponent; at least one monitoring component configured to generateperformance data; an air compression device coupled to said at least onecontrol component and configured to receive energy from an energy sourceto create a volume of high-pressured air from an ambient air source; acycling tank array comprising at least two storage tanks, such that eachstorage tank is coupled to said at least one monitoring component, saidat least one control component, and said air compression device; a fillorder component coupled to said at least one control component, andcomprising a first cycling algorithm configured to determine whichstorage tank receives a portion of high-pressured air based on saidperformance data; a release order component coupled to said at least onecontrol component, and comprising a second cycling algorithm configuredto determine which storage tank releases the portion of high-pressuredair based on said performance data; an air motor coupled to said atleast one control component and the cycling tank array; a generatorcoupled to said at least one control component and the air motor; aboost impulse component coupled to said at least one control component,the air compression device, and the air motor; a distribution apparatusconnected to said at least one control component, the generator, and anelectrical power grid; a controller connected to said at least onecontrol component and an Internet source, and comprising: a processingcomponent that determines future energy demand; a machine learningcomponent that determines a continuum of optimal operating parameters; aperformance component that determines when to capture energy, when tostore energy, and when to generate energy; and an integration componentthat incorporates the continuum of optimal operating parameters tocontrol at least one control component and at least one monitoringcomponent.
 2. The energy management machine according to claim 1,wherein said control component comprises at least one of the following:an actuator valve, a dutch, and a pressure regulator.
 3. The energymanagement machine according to claim 2, wherein at least one monitoringcomponent comprises either a temperature sensor or a pressure sensor. 4.The energy management machine according to claim 3, wherein the boostimpulse component is a tank configured to receive a second volume ofhigh-pressured air from the air compression device to initiate the airmotor from the resting position to reach an operational speed anddiscontinues once the air motor reaches the operational speed.
 5. Theenergy management machine according to claim 3, wherein the boostimpulse component is a spring apparatus configured to initiate the airmotor.
 6. The energy management machine according to claim 4, furthercomprising: an HVAC exchanger coupled to the controller and configuredto receive captured heat created by the air compression device, and aradiator coupled to the controller and configured to receive coolingcreated by electrical energy generation, wherein the controllerleverages the captured heat and cooling to improve performance andconservation of heating and cooling operations of an external building.7. The energy management machine according to claim 6, wherein theutility grid and the electrical power grid are the same energy sourceand wherein the controller further comprises a cost analysis algorithmthat controls the capture of energy from the energy source when theenergy is at an initial rate and the electrical energy is distributedwhen energy is at a subsequent rate that is higher than the initialrate.
 8. The energy management machine according to claim 6, wherein atleast one energy source comprises a utility grid, a solar cell, or awind turbine.
 9. The energy management machine according to claim 8,wherein the wind turbine comprises a mechanically-coupled transmissionoperable to convey rotational energy from a multiple of wind vaneswithin the wind turbine directly to compress ambient air for storageinto the cycling tank array.
 10. The energy management machine accordingto claim 6, wherein the environmental force is a thermal equilibriumforce.
 11. A method of managing energy generation, the method comprisingthe steps of: operating a controller to obtain information from aninternet source, wherein the information includes energy supply anddemand data, energy cost data, curtailment data, and weather data;capturing energy from at least one energy source; using the capturedenergy to compress ambient air into a volume of high-pressured air;storing the high-pressured volume of air in a cycling tank arraycomprising at least two storage tanks such that the storage tanks arefilled one at a time according to a fill order; monitoring thetemperature and pressure values of each storage tank, wherein thetemperature of each storage tank is changed by an environmental force;determining the fill order based on the lowest temperature and thelowest pressure of each storage tank within the cycling tank array;determining a release order for the cycling tank array based on thehighest temperature and the highest pressure of each storage tank withinthe cycling tank array; filling each storage tank with thehigh-pressured air according to the fill order and releasing thehigh-pressured air from each storage tank one at a time according to therelease order; operating a boost impulse to initiate an air motor from aresting position to an operational speed and discontinuing the boostimpulse once the air motor reaches the operational speed; once the airmotor reaches the operational speed, controlling the release of thehigh-pressured air from the cycling tank array to operate the air motorcoupled to a generator to create a quantity of electrical energy;deriving performance data from a system monitoring process; analyzingsaid information from an internet source to calculate future energydemand data; using machine learning algorithms to process the futureenergy demand data with the performance data to determine a continuum ofoptimal operating parameters; integrating the continuum of optimaloperating parameters to determine when to capture energy, when to storeenergy, and when to generate energy; controlling the distribution of thequantity of electrical energy to an electrical power grid.
 12. Themethod of managing energy generation according to claim 11 that uses oneor more of the following control components: an actuator valve, aclutch, and a pressure regulator.
 13. The method of managing energygeneration according to claim 12 that uses one or more of a temperaturesensor and a pressure sensor.
 14. The method of managing energygeneration according to claim 13, wherein the boost impulse is a tankconfigured to receive a second volume of high-pressured air to initiatethe air motor from the resting position to reach an operational speedand discontinues once the air motor reaches the operational speed. 15.The method of managing energy generation according to claim 13, whereinthe boost impulse is a spring apparatus configured to initiate the airmotor.
 16. The method of managing energy generation according to claim14 that uses: an HVAC exchanger coupled to the controller and configuredto receive captured heat created by an air compression device, and aradiator coupled to the controller and configured to receive coolingcreated by electrical energy generation, leveraging the captured heatand cooling to improve performance and conservation of heating andcooling operations of an external building.
 17. The method of managingenergy generation according to claim 16, wherein the utility grid andthe electrical power grid are the same energy source and wherein thecontroller further comprises a cost analysis algorithm that controls thecapture of energy from the energy source when the energy is at aninitial rate and the electrical energy is distributed when energy is ata subsequent rate that is higher than the initial rate.
 18. The methodof managing energy generation according to claim 16, wherein at leastone energy source comprises a utility grid, a solar cell, or a windturbine.
 19. The method of managing energy generation according to claim18, wherein the wind turbine comprises a mechanically-coupledtransmission operable to convey rotational energy from a multiple ofwind vanes within the wind turbine directly to compress ambient air forstorage into the cycling tank array.
 20. The method of managing energygeneration according to claim 16, wherein the environmental force is athermal equilibrium force.